static void Main(string[] args)
{
Population populacja = new Population(5, 6, n => n * n, new Random());
int ilosc = 5;//ilosc pokoleń
for (int i = 0; i < ilosc; i++)
{
for (int j = 0; j <= 2; i++)
{
foreach (var z in populacja.Individuals)
{
foreach (var x in populacja.Individuals)
{
var crossoverOperator = new OnePointCrossover(new Random());
crossoverOperator.Crossover(z, x);
}
}
}
var lambda = new OnePointCrossover(new Random());
var pokolenie = lambda.IndividualsLambda;
foreach (var y in pokolenie)
{
populacja.Individuals.Add(y);
}
var top = populacja.Individuals.OrderByDescending(x => x.Fitness).Take(5);
populacja = null;
populacja.Individuals.Add(top);
}
}
static void Main(string[] args)
{
Stopwatch sw = new Stopwatch();
sw.Start();
Func <double, double> fitness = x => 2 * x + 1;
var ga = new GeneticAlgorithm(1500, 500,
new OnePointCrossover(),
new ClassicMutationOperator(),
new RouletteWheelSelection(),
fitness);
//ga.PrintStatistics = true;
var result = ga.RunSimulation(500);
Console.WriteLine($"x = {result.Chromosome.DecodedValue}, f = {result.Fitness}");
var ind = new Individual(10);
ind.ReplaceGenes(new bool[] { true, true, true, true, true, true, true, true, true, true });
Console.WriteLine($"x = {ind.Chromosome.DecodedValue}, f = {fitness(ind.Chromosome.DecodedValue)}");
sw.Stop();
Console.WriteLine(sw.ElapsedMilliseconds + "ms");
Console.ReadLine();
return;
var ind1 = new Individual(10);
var ind2 = new Individual(10);
Console.WriteLine("Individual 1: ");
Console.WriteLine(ind1);
Console.WriteLine("Individual 2: ");
Console.WriteLine(ind2);
var crossover = new OnePointCrossover();
crossover.Crossover(ind1, ind2);
Console.WriteLine("Individual 1: ");
Console.WriteLine(ind1);
Console.WriteLine("Individual 2: ");
Console.WriteLine(ind2);
var mutation = new ClassicMutationOperator();
Console.WriteLine("--Before mutation: ");
Console.WriteLine("Individual 1: ");
Console.WriteLine(ind1);
mutation.Mutation(ind1, 0.1);
Console.WriteLine("--After mutation: ");
Console.WriteLine("Individual 1: ");
Console.WriteLine(ind1);
}
public void OnePointCrossoverTest()
{
IChromosome<int> a = new DigitalChromosome().GenerateFromArray(new int[] { 1, 2, 3, 4 });
IChromosome<int> b = new DigitalChromosome().GenerateFromArray(new int[] { 4, 3, 2, 1 });
OnePointCrossover<int> cross = new OnePointCrossover<int>(2);
IChromosome<int>[] res = cross.Crossover(a, b);
IChromosome<int>[] exp = new IChromosome<int>[2] { new DigitalChromosome().GenerateFromArray(new int[] { 1, 2, 2, 1 }), new DigitalChromosome().GenerateFromArray(new int[] { 4, 3, 3, 4 }) };
CollectionAssert.AreEqual(res, exp);
}
public void NoCommonRegionCrossoverTest()
{
var converter = new MathExpressionConverter(MathPrimitiveSets.Default);
var prog1 = converter.FromNormalNotation("sin((1-2))");
var prog2 = converter.FromNormalNotation("((2+3)-1)");
Console.WriteLine($"{prog1}, {prog2}");
var op = new OnePointCrossover <MathProgram, double>();
var allProgs = new HashSet <MathProgram>(op.GetAllOffspring(prog1, prog2));
var prog = op.Crossover(prog1, prog2);
Assert.IsTrue(allProgs.Contains(prog2),
$"{prog} should be a valid crossover between {prog1} and {prog2}.");
Assert.IsTrue(prog.Equals(prog2),
$"{prog} should be the only valid crossover between {prog1} and {prog2}.");
Assert.AreEqual(allProgs.Count, 1, double.Epsilon,
$"Crossover between {prog1} and {prog2} should only have 1 element.");
}
